More about PyTorch

At a granular level, PyTorch is a library that consists of the following components:

torch

a Tensor library like NumPy, with strong GPU support

torch.autograd

a tape-based automatic differentiation library that supports all differentiable Tensor operations in torch

torch.nn

a neural networks library deeply integrated with autograd designed for maximum flexibility

torch.multiprocessing

Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training.

torch.utils

DataLoader, Trainer and other utility functions for convenience

torch.legacy(.nn/.optim)

legacy code that has been ported over from torch for backward compatibility reasons

Usually one uses PyTorch either as:

a replacement for NumPy to use the power of GPUs.

a deep learning research platform that provides maximum flexibility and speed

Elaborating further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a ndarray).

PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate
compute by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs
such as slicing, indexing, math operations, linear algebra, reductions.
And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world.
One has to build a neural network, and reuse the same structure again and again.
Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to
change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes
from several research papers on this topic, as well as current and past work such as
torch-autograd,
autograd,
Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
You get the best of speed and flexibility for your crazy research.

Python First

PyTorch is not a Python binding into a monolithic C++ framework.
It is built to be deeply integrated into Python.
You can use it naturally like you would use NumPy / SciPy / scikit-learn etc.
You can write your new neural network layers in Python itself, using your favorite libraries
and use packages such as Cython and Numba.
Our goal is to not reinvent the wheel where appropriate.

Imperative Experiences

PyTorch is designed to be intuitive, linear in thought and easy to use.
When you execute a line of code, it gets executed. There isn't an asynchronous view of the world.
When you drop into a debugger, or receive error messages and stack traces, understanding them is straightforward.
The stack trace points to exactly where your code was defined.
We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.

Fast and Lean

PyTorch has minimal framework overhead. We integrate acceleration libraries
such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed.
At the core, its CPU and GPU Tensor and neural network backends
(TH, THC, THNN, THCUNN) are written as independent libraries with a C99 API.
They are mature and have been tested for years.

Hence, PyTorch is quite fast – whether you run small or large neural networks.

The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives.
We've written custom memory allocators for the GPU to make sure that
your deep learning models are maximally memory efficient.
This enables you to train bigger deep learning models than before.

Extensions without Pain

Writing new neural network modules, or interfacing with PyTorch's Tensor API was designed to be straightforward
and with minimal abstractions.

If you want to write your layers in C/C++, we provide an extension API based on
cffi that is efficient and with minimal boilerplate.
There is no wrapper code that needs to be written. You can see a tutorial here and an example here.

Installation

Binaries

Commands to install from binaries via Conda or pip wheels are on our website:

From Source

If you are installing from source, we highly recommend installing an Anaconda environment.
You will get a high-quality BLAS library (MKL) and you get a controlled compiler version regardless of your Linux distro.

Install PyTorch

set"VS150COMNTOOLS=C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build"setCMAKE_GENERATOR=Visual Studio 152017 Win64
setDISTUTILS_USE_SDK=1REM The following line is needed for Python 2.7, but the support for it is very experimental.setMSSdk=1call"%VS150COMNTOOLS%\vcvarsall.bat" x64 -vcvars_ver=14.11
python setup.py install

Docker image

Dockerfile is supplied to build images with cuda support and cudnn v7. Build as usual

docker build -t pytorch -f docker/pytorch/Dockerfile .

You can also pull a pre-built docker image from Docker Hub and run with nvidia-docker,
but this is not currently maintained and will pull PyTorch 0.2.

nvidia-docker run --rm -ti --ipc=host pytorch/pytorch:latest

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.
for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you
should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

Previous Versions

Installation instructions and binaries for previous PyTorch versions may be found
on our website.

Communication

Slack: general chat, online discussions, collaboration etc. https://pytorch.slack.com/ . Our slack channel is invite-only to promote a healthy balance between power-users and beginners. If you need a slack invite, ping us at [email protected]

newsletter: no-noise, one-way email newsletter with important announcements about pytorch. You can sign-up here: http://eepurl.com/cbG0rv

Releases and Contributing

PyTorch has a 90 day release cycle (major releases).
Its current state is Beta, we expect no obvious bugs. Please let us know if you encounter a bug by filing an issue.

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, utility functions or extensions to the core, please first open an issue and discuss the feature with us.
Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking the core in a different direction than you might be aware of.

The Team

PyTorch is a community driven project with several skillful engineers and researchers contributing to it.